Advances in X-ray and neutron sources, as well as in area-detector technologies, enable the recording of several terabytes of raw two-dimensional detector data in a single experiment. While several efficient integration and conversion tools are available for data collected in transmission geometry, analogous solutions for grazing-incidence diffraction (including grazing-incidence X-ray diffraction and grazing-incidence wide-angle X-ray scattering) experiments have not yet achieved the same level of efficiency. The development of new data analysis tools, including machine-learning-based software for X-ray data, necessitates the establishment of a standardized format for the converted data. To address these challenges, we have developed a new Python library, pygid, which is designed to facilitate fast data processing while providing compatibility with various raw data formats, a standardized data storage format and an intuitive interface for straightforward use. pygid supports three types of coordinate systems and both transmission and grazing-incidence geometries. It is capable of handling large datasets, performing one-dimensional line cuts and simulating expected Bragg peak positions for given structures. The package facilitates sample and experimental metadata curation in accordance with the FAIR principles. As an integral part of the broader mlgid pipeline, pygid serves as the initial step linking raw scattering patterns with machine learning tools for data analysis. The pygid package is accessible at https://github.com/mlgid-project.
Abukaev et al. (Thu,) studied this question.